U.S. patent application number 16/993789 was filed with the patent office on 2021-03-04 for dynamic content recommendations.
The applicant listed for this patent is Comcast Cable Communications, LLC. Invention is credited to George Thomas Des Jardins, Erik M. Schwartz.
Application Number | 20210064647 16/993789 |
Document ID | / |
Family ID | 1000005211969 |
Filed Date | 2021-03-04 |
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United States Patent
Application |
20210064647 |
Kind Code |
A1 |
Des Jardins; George Thomas ;
et al. |
March 4, 2021 |
Dynamic Content Recommendations
Abstract
According to some aspects, disclosed methods and systems may
include determining, by a device and based on historical data
associated with a first user, a first user profile comprising one
or more content recommendation periods each associated with a time
period and a content classification, and in response to detecting a
user interaction, selecting a first content recommendation period
of the one or more content recommendation periods. The methods and
system may also include determining one or more content candidates
corresponding to the content classification from a plurality of
content assets based on an amount of remaining time in the time
period associated with the first content recommendation period and
a correlation between the historical data associated with the first
user and one or more contextual features associated with the
plurality of content assets, and transmitting, to a client device,
an indication of the one or more content candidates.
Inventors: |
Des Jardins; George Thomas;
(Washington, DC) ; Schwartz; Erik M.; (Kensington,
MD) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Comcast Cable Communications, LLC |
Philadelphia |
PA |
US |
|
|
Family ID: |
1000005211969 |
Appl. No.: |
16/993789 |
Filed: |
August 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14310327 |
Jun 20, 2014 |
10776414 |
|
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16993789 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/435
20190101 |
International
Class: |
G06F 16/435 20060101
G06F016/435 |
Claims
1. A method comprising: determining, by a computing device, a
plurality of profiles associated with a plurality of users of a
user device, wherein the plurality of profiles are associated with
a plurality of content recommendation periods, wherein each content
recommendation period is associated with a content classification;
receiving a request for a content recommendation; determining, for
each user of the plurality of users, a corresponding frequency of
using the user device; ranking the plurality of users based on the
frequencies of using the user device; and causing output of one or
more content candidates, based on: the request, the ranking, and a
first candidate content recommendation period of the plurality of
content recommendation periods.
2. The method of claim 1, further comprising: determining, based on
a current time of day, the one or more content candidates.
3. The method of claim 1, further comprising: determining the first
candidate content recommendation period based on a type of user
interaction indicated by the request for the content
recommendation.
4. The method of claim 1, further comprising: ranking the one or
more content candidates based on a correlation between historical
data associated with a first user and one or more contextual
features associated with the one or more content candidates.
5. The method of claim 4, wherein ranking the one or more content
candidates further comprises: assigning a higher rank to one or
more of the one or more content candidates having a time length
that substantially corresponds to a remaining time in the first
candidate content recommendation period.
6. The method of claim 1, further comprising: determining the first
candidate content recommendation period based on one or more of a
behavior of a first user or a location of the first user.
7. The method of claim 1, further comprising: after receiving the
request for the content recommendation, determining at least one
environmental factor; and adjusting, based on the at least one
environmental factor, one or more of a start time of the first
candidate content recommendation period or an end time of the first
candidate content recommendation period.
8. The method of claim 1, further comprising: adjusting a start
time or an end time, of the first candidate content recommendation
period, based on weather or traffic.
9. The method of claim 1, wherein the plurality of content
recommendation periods are based on one or more of contextual
features associated with: content previously accessed by a first
user; or behavior associated with the first user.
10. The method of claim 1, further comprising: after a first user
stops playback of a content asset, adjusting one or more of a start
time of the first candidate content recommendation period, an end
time of the first candidate content recommendation period, or a
first content classification associated with the first candidate
content recommendation period.
11. The method of claim 1, wherein at least two content
recommendation periods of the plurality of profiles correspond to a
same time period.
12. The method of claim 1, wherein ranking the plurality of users
comprises: determining which user, among the plurality of users,
has the highest frequency of using the user device.
13. The method of claim 1, wherein ranking the plurality of users
comprises: determining that a first user, among the plurality of
users, has a higher frequency of using the user device than a
second user among the plurality of users.
14. The method of claim 1, wherein determining the corresponding
frequency of using the user device comprises: determining a
corresponding frequency of implementation, in the user device, of a
corresponding profile of the plurality of profiles.
15. The method of claim 1, further comprising: determining the
first candidate content recommendation period based on receiving,
from the user device, data indicating a state of an application
running on the user device.
16. A method comprising: determining, by a computing device, a
plurality of profiles associated with a plurality of users of a
user device, wherein each of the plurality of profiles is
associated with a plurality of content recommendation periods,
wherein each content recommendation period is associated with a
content classification; receiving a request for a content
recommendation; determining, for each user of the plurality of
users, a corresponding frequency of using the user device;
selecting, based on determining a user, of the plurality of users,
associated with the highest frequency of the determined
frequencies, a profile of the plurality of profiles; and causing
output of one more content candidates, based on: the request, and a
first candidate content recommendation period of the plurality of
content recommendation periods of the selected profile.
17. The method of claim 16, wherein determining the corresponding
frequency of using the user device comprises: determining a
corresponding frequency of implementation, in the user device, of a
corresponding profile of the plurality of profiles.
18. The method of claim 16, wherein the selected profile comprises
a profile of the user who has used the user device the most.
19. A method comprising: receiving an indication of a user
interaction with a plurality of content assets; determining, by a
computing device and based on the indication of the user
interaction, a plurality of profiles associated with a plurality of
users of a user device, wherein each of the plurality of profiles
is associated with a plurality of content recommendation periods,
wherein each content recommendation period is associated with a
content classification; receiving a request for a content
recommendation; determining, for each of the plurality of profiles,
a corresponding frequency of use on the user device; selecting,
based on the frequencies, a profile of the plurality of profiles;
and causing output of one or more content candidates, based on: the
request, and a first candidate content recommendation period of the
plurality of content recommendation periods of the selected
profile.
20. The method of claim 19, further comprising: determining, based
on data indicating a state of an application running on the user
device, the first candidate content recommendation period.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and is a continuation of
U.S. application Ser. No. 14/310,327, filed Jun. 20, 2014, the
contents of which are incorporated herein by reference in its
entirety.
BACKGROUND
[0002] Content providers have long sought to determine how to
quickly produce accurate content recommendations for users. There
is an ever-present need to accurately produce content
recommendations that fit into a user's daily schedule, while
maximizing the amount of time the user has available to access
those content recommendations.
SUMMARY
[0003] The following summary is for illustrative purposes only, and
is not intended to limit or constrain the detailed description.
[0004] One or more aspects of the disclosure provide for a method
that may include determining, by a device and based on historical
data associated with a first user, a first user profile comprising
one or more content recommendation periods each associated with a
time period and a content classification; and in response to
detecting a user interaction, selecting a first content
recommendation period of the one or more content recommendation
periods. The method may also include determining one or more
content candidates corresponding to the content classification from
a plurality of content assets based on an amount of remaining time
in the time period associated with the first content recommendation
period and a correlation between the historical data associated
with the first user and one or more contextual features associated
with the plurality of content assets; and transmitting, to a client
device, an indication of the one or more content candidates.
[0005] One or more aspects of the disclosure also provide for a
method that may include determining, by a device and based on
historical data associated with a first user and a second user, a
first user profile and a second user profile each comprising one or
more content recommendation periods associated with a time period
and a content classification; and in response to detecting an
interaction by the first user or the second user, assigning a first
content recommendation period from one of the first user profile
and the second user profile. The method may also include
determining one or more content candidates corresponding to the
content classification from a plurality of content assets based on
an amount of remaining time in the time period associated with the
first content recommendation period and a correlation between one
or more contextual features associated with the plurality of
content assets and the historical data associated with at least one
of the first user and the second user; and transmitting, to a
client device, an indication of the one or more content
candidates.
[0006] One or more aspects of the disclosure also provide for a
method that may include determining, by a device and based on a
user interaction and historical data associated with a first user,
one or more content recommendation periods; assigning one or more
content candidates to the one or more content recommendation
periods based on a correlation between the historical data
associated with the first user and one or more contextual features
associated with the one or more content candidates; and
transmitting an indication associated with the one or more content
candidates.
[0007] The summary here is not an exhaustive listing of the novel
features described herein, and is not limiting of the claims. These
and other features are described in greater detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] These and other features, aspects, and advantages of the
present disclosure will become better understood with regard to the
following description, claims, and drawings. The present disclosure
is illustrated by way of example, and not limited by, the
accompanying figures in which like numerals indicate similar
elements.
[0009] FIG. 1 illustrates an example communication network on which
various features described herein may be used.
[0010] FIG. 2 illustrates an example computing device that can be
used to implement any of the methods, servers, entities, and
computing devices described herein.
[0011] FIG. 3 illustrates an example system in accordance with
aspects of the present disclosure.
[0012] FIG. 4 illustrates an example flow diagram of a method in
accordance with aspects of the present disclosure.
[0013] FIG. 5 illustrates an example flow diagram of a method in
accordance with aspects of the present disclosure.
DETAILED DESCRIPTION
[0014] As will be appreciated by one of skill in the art upon
reading the following disclosure, various aspects described herein
may be embodied as a method, a computer system, or a computer
program product. Accordingly, those aspects may take the form of an
entirely hardware embodiment, an entirely software embodiment or an
embodiment combining software and hardware aspects. Furthermore,
such aspects may take the form of a computer program product stored
by one or more computer-readable storage media having
computer-readable program code, or instructions, embodied in or on
the storage media. Any suitable computer readable storage media may
be utilized, including hard disks, CD-ROMs, optical storage
devices, removable storage media, solid state memory, RAM, magnetic
storage devices, and/or any combination thereof. In addition, the
functionality may be embodied in whole or in part in firmware or
hardware equivalents, such as integrated circuits, field
programmable gate arrays (FPGAs), and the like. Various signals
representing data or events as described herein may be transferred
between a source and a destination in the form of electromagnetic
waves traveling through signal-conducting media such as metal
wires, optical fibers, and/or wireless transmission media (e.g.,
air and/or space).
[0015] FIG. 1 illustrates an example communication network 100 on
which many of the various features described herein may be
implemented. The network 100 may be any type of information
distribution network, such as satellite, telephone, cellular,
wireless, etc. One example may be an optical fiber network, a
coaxial cable network, or a hybrid fiber/coax distribution network.
Such networks 100 use a series of interconnected communication
links 101 (e.g., coaxial cables, optical fibers, wireless, etc.) to
connect multiple premises 102 (e.g., businesses, homes, consumer
dwellings, etc.) to a local office or headend 103. The local office
103 may transmit downstream information signals onto the links 101,
and each premises 102 may have a receiver used to receive and
process those signals.
[0016] There may be one or more links 101 originating from the
local office 103, and it may be split a number of times to
distribute the signal to the various premises 102 in the vicinity
(which may be many miles) of the local office 103. The links 101
may include components not illustrated, such as splitters, filters,
amplifiers, etc. to help convey the signal clearly, but in general
each split introduces a bit of signal degradation. Portions of the
links 101 may also be implemented with fiber-optic cable, while
other portions may be implemented with coaxial cable, other lines,
or wireless communication paths. By running fiber optic cable along
some portions, for example, signal degradation may be significantly
minimized, allowing a single the local office 103 to reach even
farther with its network of the links 101 than before.
[0017] The local office 103 may include an interface 104, such as a
termination system (TS). More specifically, the interface 104 may
be a cable modem termination system (CMTS), which may be a
computing device configured to manage communications between
devices on the network of the links 101 and backend devices such as
the servers 105-107 (to be discussed further below). The interface
104 may be as specified in a standard, such as the Data Over Cable
Service Interface Specification (DOCSIS) standard, published by
Cable Television Laboratories, Inc. (a.k.a. CableLabs), or it may
be a similar or modified device instead. The interface 104 may be
configured to place data on one or more downstream frequencies to
be received by modems at the various premises 102, and to receive
upstream communications from those modems on one or more upstream
frequencies.
[0018] The local office 103 may also include one or more network
interfaces 108, which can permit the local office 103 to
communicate with various other external networks 109. These
networks 109 may include, for example, networks of Internet
devices, telephone networks, cellular telephone networks, fiber
optic networks, local wireless networks (e.g., WiMAX), satellite
networks, and any other desired network, and the network interface
108 may include the corresponding circuitry needed to communicate
on the external networks 109, and to other devices on the network
such as a cellular telephone network and its corresponding cell
phones.
[0019] As noted above, the local office 103 may include a variety
of servers 105-107 that may be configured to perform various
functions. For example, the local office 103 may include one or
more push notification servers 105. The push notification server
105 may generate push notifications to deliver data and/or commands
to the various premises 102 in the network (or more specifically,
to the devices in the premises 102 that are configured to detect
such notifications).
[0020] The local office 103 may also include one or more content
servers 106. The content server 106 may be one or more computing
devices that are configured to provide content to users at their
premises. This content may be, for example, advertisements (such as
commercials), video on demand movies, television programs, songs,
text listings, etc. The content server 106 may include software to
validate user identities and entitlements, to locate and retrieve
requested content, to encrypt the content, and to initiate delivery
(e.g., streaming or downloading) of the content to the requesting
user(s) and/or device(s). The content server 106 may also be
configured to generate advertising decisions and rules, and
transmit them to a requesting user or device.
[0021] The local office 103 may also include one or more
application servers 107. An application server 107 may be a
computing device configured to offer any desired service, and may
run various languages and operating systems (e.g., servlets and JSP
pages running on Tomcat/MySQL, OSX, BSD, Ubuntu, Redhat, HTML5,
JavaScript, AJAX and COMET). For example, an application server may
be responsible for collecting television program listings
information and generating a data download for electronic program
guide listings. As another example, the application server or
another server may be responsible for monitoring user viewing
habits and collecting that information for use in selecting
advertisements. As another example, the application server or
another server may be responsible for formatting and inserting
advertisements in, for example a video stream being transmitted to
the premises 102. Yet the application server or another application
server may be responsible for associating interactive components
into and with content and/or advertisements. Although shown
separately, one of ordinary skill in the art will appreciate that
the push server 105, the content server 106, and the application
server 107 may be combined. Further, here the push server 105, the
content server 106, and the application server 107 are shown
generally, and it will be understood that they may each contain
memory storing computer executable instructions to cause a
processor to perform steps described herein and/or memory for
storing data.
[0022] An example premises 102a, such as a home, may include an
interface 120. The interface 120 can include any communication
circuitry needed to allow a device to communicate on one or more
links 101 with other devices in the network. For example, the
interface 120 may include a modem 110, which may include
transmitters and receivers used to communicate on the links 101 and
with the local office 103. The modem 110 may be, for example, a
coaxial cable modem (for coaxial cable lines 101), a fiber
interface node (for fiber optic lines 101), twisted-pair telephone
modem, cellular telephone transceiver, satellite transceiver, local
Wi-Fi router or access point, or any other desired modem device.
Also, although only one modem is shown in FIG. 1, a plurality of
modems operating in parallel may be implemented within the
interface 120. Further, the interface 120 may include a gateway
interface device 111. The modem 110 may be connected to, or be a
part of, the gateway interface device 111. The gateway interface
device 111, such as a gateway, may be a computing device that
communicates with the modem(s) 110 to allow one or more other
devices in the premises 102a, to communicate with the local office
103 and other devices beyond the local office 103. The gateway
interface device 111 may be a set-top box, digital video recorder
(DVR), computer server, or any other desired computing device. The
gateway interface device 111 may also include (not shown) local
network interfaces to provide communication signals to requesting
entities/devices in the premises 102a, such as the display devices
112 (e.g., televisions), the additional set-top boxes or the DVRs
113, the personal computers 114, the laptop computers 115, the
wireless devices 116 (e.g., wireless routers, wireless laptops,
notebooks, tablets and netbooks, cordless phones (e.g., Digital
Enhanced Cordless Telephone--DECT phones), mobile phones, mobile
televisions, personal digital assistants (PDA), etc.), the landline
phones 117 (e.g. Voice over Internet Protocol--VoIP phones), and
any other desired devices. Examples of the local network interfaces
include Multimedia Over Coax Alliance (MoCA) interfaces, Ethernet
interfaces, universal serial bus (USB) interfaces, wireless
interfaces (e.g., IEEE 802.11, IEEE 802.15), analog twisted pair
interfaces, Bluetooth interfaces, and others.
[0023] FIG. 2 illustrates general hardware elements that can be
used to implement any of the various computing devices discussed
herein. The computing device 200 may include one or more processors
201, which may execute instructions of a computer program to
perform any of the features described herein. The instructions may
be stored in any type of computer-readable medium or memory, to
configure the operation of the processor 201. For example,
instructions may be stored in a read-only memory (ROM) 202, a
random access memory (RAM) 203, a removable media 204, such as a
Universal Serial Bus (USB) drive, compact disk (CD) or digital
versatile disk (DVD), floppy disk drive, or any other desired
storage medium. Instructions may also be stored in an attached (or
internal) storage 205, such as a hard drive. The computing device
200 may include one or more output devices, such as a display 206
(e.g., an external television), and may include one or more output
device controllers 207, such as a video processor. There may also
be one or more user input devices 208, such as a remote control,
keyboard, mouse, touch screen, microphone, etc. The computing
device 200 may also include one or more network interfaces, such as
a network input/output (I/O) circuit 209 (e.g., a network card) to
communicate with an external network 210. The network input/output
circuit 209 may be a wired interface, wireless interface, or a
combination of the two. In some embodiments, the network
input/output circuit 209 may include a modem (e.g., a cable modem),
and the external network 210 may include the communication links
101 discussed above, the external network 109, an in-home network,
a provider's wireless, coaxial, fiber, or hybrid fiber/coaxial
distribution system (e.g., a DOCSIS network), or any other desired
network. Additionally, the device may include a location-detecting
device, such as a global positioning system (GPS) microprocessor
211, which can be configured to receive and process global
positioning signals and determine, with possible assistance from an
external server and antenna, a geographic position of the
device.
[0024] FIG. 2 shows an example hardware configuration.
Modifications may be made to add, remove, combine, divide, etc.,
components as desired, and some or all of the elements may be
implemented using software. Additionally, the components
illustrated may be implemented using basic display devices and
components, and the same components (e.g., the processor 201, the
ROM 202, the display 206, other input/output devices, etc.) may be
used to implement any of the other display devices and components
described herein. For example, the various components herein may be
implemented using display devices having components such as a
processor executing computer-executable instructions stored on a
computer-readable medium (e.g., the storage 205), as illustrated in
FIG. 2.
[0025] Having described examples of network environments and
content consumption devices that may be used in implementing
various aspects of the disclosure, several examples will now be
described in greater detail illustrating how a display device may
monitor user actions during an advertisement, a display device may
restrict a user's control of the display device during an
advertisement, and efficacy file reports are created and used. The
consumption device, which may be a user's tablet computer, personal
computer, smartphone, DVR, or any other computing device as
described herein, may monitor any client-side interaction with the
user during an advertisement, such as detecting a change in audio
level or order of display elements. In other examples, the display
device may prohibit a user from muting an advertisement during
play.
[0026] FIG. 3 illustrates an example system 300 in accordance with
one or more disclosed features described herein. The system 300 may
include the client device 316. The client device 316 may comprise,
be substantially similar to, and/or be the same as computing device
200, as shown in FIG. 2. The client device 316 may comprise, for
example, a set-top box 113, personal computer 114, laptop computer
115, gateway 111, modem 110, display device 112, landline phone
117, wireless device 116, a mobile device (smartphone, tablet,
smartwatch, Bluetooth, etc.), digital video recorder (DVR), digital
video player, audio device, or any other device capable of
providing or accessing media and/or content, or combinations
thereof. The client device 316 may be operably connected to an
input device 314, which may comprise a remote control, keyboard,
mouse, touch screen, microphone, or the like used to control and
access features of the client device 316. The input device 314 m
may comprise, be substantially similar to, and/or be the same as
input device 208, as shown in FIG. 2. One or more users, such as
the user 308, the user 310, and the user 312, may interact with the
input device 314 and/or the client device 316. The users 308, 310,
and 312 may also interact and/or be associated with one or more
computing devices, such as the computing devices 302, 304, and 306.
The computing devices 302, 304, and 306 may comprise, be
substantially similar to, and/or be the same as computing device
200, as shown in FIG. 2. The computing devices 302, 304, and 306
may comprise, for example, a set-top box 113, personal computer
114, laptop computer 115, gateway 111, modem 110, display device
112, landline phone 117, wireless device 116, a mobile device
(smartphone, tablet, smartwatch, Bluetooth, etc.), digital video
recorder (DVR), digital video player, audio device, or any other
device capable of providing or accessing media and/or content, or
combinations thereof.
[0027] The client device 316 may be operably connected to the local
office 103 via a network 318. The network 318 may comprise, be
substantially similar to, and/or be the same as network 100, link
101, external network 109, and/or external network 210 as shown in
FIGS. 1 and 2. The network 318 may be, for example, a wireless
network, a MoCA in-home coaxial cable network, a cellular network,
an Ethernet network, a Wi-Fi network, and the like. The local
office 103, which may be associated with a head end, may provide
content to the client device 316 via the network 318. The local
office 103 may include and/or be associated with one or more
profiles, such as the user profile 322, the user profile 324, and
the user profile 326. Each user profile may be associated with the
users 308, 310, or 312. Each user profile may be associated with a
user's historical data, such as the historical data 328, the
historical data 330, and the historical data 332 (each of which may
correspond to the users 308, 310, and 312). It is noted that while
the local office 103 is shown in FIG. 3 to include profiles 322,
324, and 326 along with the historical data 328, 330, and 332,
profiles 322, 324, and 326 and the historical data 328, 330, and
332 may be included in and/or associated with other components,
such as the client device 316. As previously shown in FIG. 1, the
local office 103 may include one or more content servers 106. The
local office 103 (e.g., via the content server 106) may also access
and retrieve content from one or more content sources 334, such as
internet content sources, music content sources, or video content
sources, for transmission to one or more devices, such as the
client device 316.
[0028] Each component of the system 300 may be operably connected
to and/or interact with each other via a direct and/or indirect
connection, such as via a network or hardwire. Each component of
the system 300 may be affiliated, operably connected to, and/or
located at a service or content provider, such as the local office
103.
[0029] FIG. 3 illustrates one client device 316, however, any
number of client devices, such as two, ten, or a hundred, may be
included in the system 300 and/or in any of the embodiments
disclosed herein. The client device 316 may be located at a
location, such as premises 102a. Additionally, client devices may
be located at a same or similar location, such as premises 102a, or
may be located at different locations. The client device 316 may
provide and/or access content services, such as video/image content
services, audio content services, internet content services, and
the like. The client device 316 may access content services and
other services via, for example, a video processor or audio
processor (e.g., similar to device controller 207) and may display
content on a display (e.g., similar to display 206 as shown in FIG.
2). For example, the client device 316 may launch an application on
the client device 316, and access content via the launched
application. The client device 316 may access content on any number
of content platforms, which may include a linear content platform,
media (video/audio) on-demand content platform, mobile content
platform, a service provider-specific content platform, an online
content platform, or other content platform that may be capable of
providing content on the client device 316, or combinations
thereof. For example, the client device 316 may be a mobile device,
and may provide content, such as a movie, through a mobile
application. In such a scenario, the content may be provided
through a mobile content platform. In another example, the client
device 316 may be a set-top box, and may provide content, such as a
television program or show, via linear content (e.g., live
broadcast). In such a scenario, the content may be provided through
a linear content platform. In yet another example, the client
device 316 may be a set-top box, and may provide content, such as a
song, using a media on-demand content platform, and/or may provide
content, such as an internet video, using an online content
platform. A service provider may provide content that may be
specific for that service provider with the service provider's own
content platform. For example, content provided on a service
provider content platform may be customized by a service provider
for a particular client device and/or user, such as providing a
user's favorite part of a movie, recommended scene of a movie, and
the like. Additionally, content provided on a service provider
content platform may be a combination of various other platforms,
such as combining online content with linear or video on-demand
content.
[0030] Users 308, 310, and 312 may access content from the client
device 316. For example, the user 308 may request access to a movie
on a set-top box, and subsequently watch the movie on a display
connected to the set-top box. In this example, the user 308 may use
an input device 314 (such as a remote control) to request access to
the movie on the set-top box. According to some aspects, the local
office 103 may then determine and/or access the historical data
328, 330, and 332 corresponding to each of the users 308, 310, and
312. The historical data 328 may include the viewing patterns
and/or behavior of the user 308, as well as features of any
viewed/accessed content, such as the content's metadata and/or
contextual features (e.g., state of the content and the actual
information embedded in the content). In addition, the historical
data 328 may also include the relationship between metadata or
contextual information from a show and how they affect the viewing
patterns or behavior of the user 308.
[0031] The historical data 328 (e.g., metadata and/or contextual
features of content and/or behavior of a user) may include the time
or date associated with an accessed content item (such as if a show
comes on at 21:00), a particular day of the week associated with a
content item (such as if a new episode of a show only comes on
Tuesdays), whether there are any exciting moments or events in the
content (such as the number of highlight plays in a football game),
or other descriptive information for content (such as a score of a
football game at any point during the football game). Other
information that may be included in the historical data 328 may
include the name or description of the content item, the content
item's platform or channel, content item's location (e.g., IP
address), a category/type/classification for the content (such as
kids, movie, sports, drama, sitcom, comedy, news, and the like),
whether the content is broadcasted live, and the like. The
historical data 328 may also include a DVR list of content for a
user, which shows the user typically records, which shows the user
typically watches live. The historical data 328 may also include
which applications the user accesses (such as a news, traffic,
weather, shopping, gaming, fantasy sports, or music application)
and what time the user accesses those applications.
[0032] Contextual information may include any descriptive
information regarding what is actually happening in a show or
application. For example, if a user is watching a football game,
contextual information may include that the user's favorite team is
losing 49-7 and the game is in the second quarter. In such a
situation, if the user then opts to change to another program, then
the user's behavior may be determined to include changing the
channel when the user's favorite team is losing 49-7 in the second
quarter. The user's favorite team may be determined by any number
of factors, such as a correlation between the location associated
with a team and of the user, how many games the user has watched of
a particular team, and whether the user has input the user's
favorite team into the client device 316 and/or the local office
103. It is noted that while the user 308 and the user 308's
historical data 328 are used in the above examples, other users,
such as the users 310 and 312, and their historical data, such as
the historical data 330 and 332, may also be used in the examples
and embodiments disclosed herein. In addition, the historical data
328 (e.g., information regarding the user 308's behavior) may also
include whether the user 308 watched a show for the entire
scheduled length of the show (or accessed a song for the entire
length of the song), which portion of a show the user watched
(e.g., beginning, middle, end), how much of a show a user watched
(e.g., 20% or 50%), whether and at which point during the show the
user stopped accessing the show and/or changed to another show. A
user's historical data will be described below in more detail.
[0033] Using the historical data 328, 330, and 332, the local
office 103 may then create and/or store one or more user profiles
322, 324, and 326 which may correspond to each of the users 308,
310, and 312. A user profile 322 may include a schedule describing
when and/or at what times during a period of time (e.g., an hour,
day, week, month, year, etc.) the user 308 accesses/watches
content. For example, the user profile 322 may include a schedule
detailing which parts of the day the user 308 tends to access or
frequently accesses content (such as time periods during the day),
and which types of content the user 308 tends to watch or
frequently watches during those parts of the day (such as watching
kids programing from 16:00 to 17:00). Tending to access content may
describe a situation in which the user 308 accesses a content a
larger part of the time (such as 20%, 50%, 70%, and 90% of the time
and the like).
[0034] The user profile 322 may include different schedules for
different time periods, such as a different schedule for each day
of the week. For example, if the user 308 tends to watch
professional football on Mondays from 19:00-22:00, Thursdays at
20:00-23:00, and Sundays at 19:00-22:00, then a schedule for
Monday, Thursday, and Sunday may include professional football on
Mondays from 19:00-22:00, Thursdays at 20:00-23:00, and Sundays at
19:00-22:00. Consequently, a schedule for Tuesday, Wednesday,
Friday, and Saturday may not include professional football, and may
contain other content types beginning at 20:00 or 21:00 on those
days. Additionally, some content items may only be accessible
during particular times of the year, such as professional American
football being available August through February. Thus, a profile
may be updated to reflect any seasonal variation in content
programing. Also, the local office 103 may create and maintain a
profile for one or more devices associated with a user. For
example, the local office 103 may create a profile for the user 308
for a set-top box, a separate profile for the user 308 for a
smartphone, and a separate profile for the user 308 for a tablet.
It is noted that while the user 308 and the user 308's profile 322
are used in the above examples, other users, such as users 310 and
312 and their corresponding profiles (e.g., profiles 324 and 326)
may also be used in the examples and embodiments disclosed herein.
A user's profile will described below in more detail.
[0035] FIG. 4 is an exemplary flow diagram illustrating an example
process 400 in accordance with one or more disclosed features
described herein. In one or more embodiments, the process
illustrated in FIG. 4 and/or one or more steps thereof may be
performed by one or more computing devices (e.g., the input device
314, the client device 316, computing devices, 302, 304, and 306,
the local office 103, one or more content providers, and the like).
In other embodiments, the process illustrated in FIG. 4 and/or one
or more steps thereof may be embodied in computer-executable
instructions that are stored in a computer-readable medium, such as
a non-transitory computer-readable memory. The steps in this flow
diagram need not all be performed in the order specified and some
steps may be omitted and/or changed in order.
[0036] In the example provided, the process 400 may begin with step
402, in which the local office 103 may receive and analyze one or
more content assets to determine various types of metadata for
those content assets. The metadata may include contextual features
of the content, such as information that describes what is
happening or what happened during a show, or what is happening or
happen in an accessed application. For example, contextual features
for a football game may include the number of highlight plays that
happened during the game (such as if the ball advanced 20 yards,
then it is a highlight play), the score at any particular point
during the game, which teams are playing, which team is winning,
whether there is a blowout score (which may be based on a
threshold, such as over 21 points), whether it is raining or
snowing, whether an unusual event happened during the game (such as
an injury or fan running onto the field), whether the game lasted
longer than an expected length of time, whether the game went into
overtime, what time is left, which quarter the game is in, and the
like. In another example, the user 308 may access a gaming
application on the client device 316 and may play a video game with
the gaming application. Thus, contextual features for the video
game and/or gaming application may include whether the user did
well or poorly in the video game (e.g., won or lost the game), the
score in the video game, the game level of the video game, how many
points a user obtained during a video session, and the like. It is
noted that the analyzed content assets may include assets
configured for any content platform, such as internet, linear, DVR,
on-demand, music, and applications (e.g., traffic, weather, social
media). Additional metadata may include whether an episode of a
show is a new or repeated episode and a content classification
(such as kids, sports, movie, drama, adult, news, etc.). The
metadata may also include the time and day the content asset may be
broadcasted live by the local office 103.
[0037] The contextual information may include whether a show
deviated from how the show is expected to happen. For example, a
children's television show, such as SpongeBob Square Pants, may not
have a lot of contextual variance from episode to episode (e.g.,
follows the same general story with little to no deviation between
episodes). Thus, contextual features may generally be the same from
episode to episode. However, as in the above football example, one
football game may be completely different than another football.
For example, the teams may be different, the score may be
different, the number and types (e.g., highlights, running plays,
passing plays, etc.) of plays may be different, the games' weather
may be different, and the like. Thus, there may be more contextual
variance between the contextual features of different sporting
events. Similarly, a drama, such as Downton Abbey, may generally
follow a similar storyline with each episode, but, there may some
variation between different episodes of Downton Abby (e.g., someone
was killed in an episode, someone was married in an episode, a
party happened in an episode, etc.).
[0038] At step 404, the local office 103 may determine, gather,
and/or analyze historical data (e.g., the historical data 328) for
a user (e.g., the user 308). The historical data 328 may include
the content viewing patterns and/or behavior of the user 308
regarding how the user 308 accesses content. For example, a user
308 may access a weather or traffic application on the client
device 316 at the user's house (e.g., premises 102a) every weekday
morning at 07:00 before the user 308 leaves to go to work. Thus,
the user 308's historical data 328 may include that the user
accesses a weather or traffic application every weekday morning at
07:00. If the user 308 then accesses a news program such as the
Today Show at 07:30 until the user 308 leaves for work at 08:00,
the historical data 328 may reflect that the user typically watches
a news program (which may specifically be the Today Show) from
07:30-08:00 on weekdays.
[0039] In some embodiments, the local office 103 may obtain other
information from the user 308, such as a location of the user. In
one example, the local office 103 may obtain location information
using a GPS device associated with the user 308 (such as found in
the client device 316, the computing device 302, or a vehicle used
by the user 308). Thus, after the user 308 leaves for work at
08:00, the local office 103 may determine how long it takes the
user 308 to arrive at a destination (e.g., an office building). All
of this information (such as location information, timing
information, user behavior, etc.) may be included in the historical
data 328. Additionally, the historical data 328 may also reflect
whether the user accessed any content on the way to work.
[0040] In some embodiments, the local office 103 may determine
historical data for a particular the client device 316. In such
cases, the historical data may not directly correspond with the
particular viewing habits and behavior of a specific user 308, but
may correspond with one or more users' behavior regarding accessing
content on that particular the client device 316. For example, a
family may include a dad (the user 308), a mom (the user 310), a
child (the user 312), and the family may own and access a set-top
box (the client device 316). The dad may access on the client
device 316 a weather or traffic application at 07:00 every weekday,
the mom and dad may access a news program (The Today Show) from
07:30-08:00 every weekday, the dad may pick up the child from
school, come home, access a kids program (SpongeBob) from
16:30-17:00 every weekday, the dad may access a gaming application
from 17:00-18:00 every weekday, the mom and dad may access the
evening local news from 18:00-19:00 every weekday, the dad may
access a sporting event (football game) from 19:00-22:00, and the
mom may access a drama or reality show (Downton Abbey or The Voice)
from 22:00-23:00. Thus, the historical data for the client device
316 may reflect all of the above information.
[0041] In some embodiments, content may be accessed on different
platforms, such as DVR, on-demand (music or video), linear, and the
like. For example, the user 308 may watch SpongeBob at 16:30 using
a video on-demand platform on weekdays. Thus, the historical data
328 may reflect that the user 308 tends to watch SpongeBob at 16:30
using a video on-demand platform on weekdays. In some embodiments,
the local office 103 may gather historical information from
scheduled and previously scheduled recordings found on a user's
DVR. For example, the user 308 may record The Today Show from
08:00-09:00 using a DVR and watch the recorded segment from
17:00-18:00 every weekday. Thus, the historical data 328 may
reflect that the user 308 tends to record the Today Show from
08:00-09:00 using a DVR and watch the recorded segment from
17:00-18:00 every weekday.
[0042] In some embodiments, the user 308 may vary the length of
time the user 308 accesses a particular content item. For example,
the user 308 may tend to watch a recording of the Today Show
17:00-18:00 every weekday. However, if there is a cooking segment
during the recorded broadcast of the Today Show, the user may tend
to stop accessing the recording (e.g., stopping the recording) when
that cooking segment approaches or happens, and may tend to switch
to a 24 hour news channel (e.g., MSNBC) until 18:00. At 18:00, the
user 308 may then tend to watch the local evening news until 19:00.
Thus, the historical data 328 may reflect that the user 308 tends
to watch a recording of the Today Show 17:00-18:00 every weekday,
but if there is a cooking segment during the recording, the user
tends to switch from the recording and watch a 24 hour news channel
(e.g., MSNBC) until 18:00, and then the local evening news from
18:00-19:00. The local office 103 may use contextual features of
the recorded content item (e.g., the Today Show) to determine that
the user 308 tends to stop watching the recording if there is a
cooking segment. For example, the local office 103 may determine
all of the times that the user 308 has stopped watching the
recording of the Today Show. The local office 103 may then look to
the contextual features of all of those recordings in which the
user 308 stopped watching. The local office 103 may then determine
that there are similarities between the episodes, such as all (or
most) of the episodes contain a cooking segment. The local office
103 may also determine at which point during the playback of the
recording the user 308 stop accessing a recording. The local office
103 may also determine which events happened immediately before and
immediately after the point in which the user 308 stopped accessing
the recording to assess any pattern or trend in the user's
behavior. Thus, the local office 103 may determine, over the course
of several recordings in which the user 308 stopped accessing,
which contextual features (such as cooking segments) are most
common in the recordings in which the user 308 stopped
accessing.
[0043] In some embodiments, the user 308 may access applications on
the client device 316. Such applications may include internet
applications, weather or traffic applications, music applications,
fitness applications, social media applications, picture sharing
application, gaming application, shopping applications, fantasy
sports applications, and the like. In such situations, the local
office 103 may include in the historical data 328 which
applications the user 308 may have accessed, and when the user 308
accessed the applications. Also, the local office 103 may also
determine contextual features of content from the applications. For
example, the local office 103 may determine to which types of music
the user 308 listens and at which times the user 308 listens to
certain types of music. The local office 103 may determine at what
point in a song a user changes to another application or song.
[0044] In some embodiments, external environmental factors, such as
traffic and weather, may affect a user's behavior in regard to
accessing content. For example, the local office 103 may determine
(and store as the historical data 328) that after the user 308
checks a traffic application, if the traffic for the user 308's
commute is good for that day, then the user 308 may listen to
music, access an application, or access content for a longer period
(e.g., longer than a scheduled time period) than the user 308 would
if the traffic was not good for that day. In a similar example, the
local office 103 may determine (and store as the historical data
328) that after the user 308 checks a weather application, if the
weather is good for that day before the user 308 leaves for the
user's commute to work, then the user 308 may listen to music,
access an application, or access content for a longer period than
the user 308 would if the weather was not good for that day.
[0045] In another example, the local office 103 may determine (and
store as the historical data 328) that when the user 308 is
accessing a gaming application on the client device 316, such as
playing a video game, that if the user 308 is doing well in the
video game (e.g., by examining contextual features, such as score,
lives, etc. of the video game), then the user 308 tends to keep
playing the video game and not access other content on the client
device 316. Additionally, the local office 103 may determine that
when the user 308 is doing poorly in the video game, then the user
308 tends to abandon the video game, and access other content on
the client device 316. In another example, the local office 103 may
determine (and store as the historical data 328) that when the user
308 is accessing a shopping application, the user tends to
concurrently access content with little contextual variance from
episode to episode (e.g., content that may follow the same general
story with little to no deviation between episodes), such as
SpongeBob Square Pants, instead of content with more contextual
variance, such as a sporting event. The local office 103 may also
determine that the user 308 tends to pause (or stop accessing) the
concurrently accessed content (e.g., SpongeBob) when the user 308
desires to access, view, and/or buy an item using the shopping
application.
[0046] At step 406, the local office 103 may use historical data
obtained in step 404 to determine a user profile describing which
types of content (e.g., sports, news, drama, movie, traffic,
gaming, etc.) a user tends to access and which times and days/dates
the user tends to access those types of content. The user profile
(e.g., the user profile 322) may correspond to a user (e.g., the
user 308) and/or a user's historical data (e.g., the historical
data 328). Thus, the local office 103 may analyze and/or use the
historical data 328 to determine and/or create a user profile 322
for the user 308. The user profile 322 may be composed of one or
more content recommendation periods. Each content recommendation
period may correspond to a time/time period and/or a content
type/classification. Each content recommendation period may also
correspond to a day or date.
[0047] For example, after analyzing the historical data 328, the
local office 103 may determine that the user 308 (or the client
device 316) tends to access on the client device 316 a traffic
application at 07:00 every weekday, access a news program from
07:30-08:00 every weekday, access a kids program from 16:30-17:00
every weekday, access the evening local news from 18:00-19:00 every
weekday, access a sporting event from 19:00-22:00 on Sundays,
Mondays, and Thursdays, and access a drama from 22:00-23:00 every
weekday. A shown in the above example, a content recommendation
period may correspond to a particular day of the week (such as the
sporting event being on Sundays, Mondays, and Thursdays). Also, a
content recommendation period may correspond to a period of time
(such as the kids program from 16:30-17:00) or a particular start
time with no particular end time (such as the traffic application
at 07:00).
[0048] FIG. 5 is an exemplary flow diagram illustrating an example
process 500 detailing exemplary steps for determining a profile, as
described in step 406 in FIG. 4. In one or more embodiments, the
process illustrated in FIG. 5 and/or one or more steps thereof may
be performed by one or more computing devices (e.g., the input
device 314, the client device 316, computing devices, 302, 304, and
306, the local office 103, one or more content providers, and the
like). In other embodiments, the process illustrated in FIG. 5
and/or one or more steps thereof may be embodied in
computer-executable instructions that are stored in a
computer-readable medium, such as a non-transitory
computer-readable memory. The steps in this flow diagram need not
all be performed in the order specified and some steps may be
omitted and/or changed in order.
[0049] FIG. 5 may begin at step 502, in which the local office 103,
using the historical data 328, may create one or more content
recommendation periods and determine for each content
recommendation period a corresponding time period, which may, for
example, be segments or portions of a day. The content
recommendation periods may be of equal lengths (e.g., 30 minutes
each) or of non-equal length (e.g., 15 minutes, 30 minutes, and 1
hour content recommendation periods). For example, the user 308 may
tend to access content on the client device 316 at 07:00 every
weekday, from 07:30-08:00 every weekday, from 16:30-17:00 every
weekday, from 18:00-19:00 every weekday, from 19:00-22:00 on
Sundays, Mondays, and Thursdays, and from 22:00-23:00 every
weekday. Thus, using this historical data 328 of the user 308, the
local office 103 may determine that as part of the user profile 322
for the user 308, content recommendation periods may be made for
07:00 every weekday, from 07:30-08:00 every weekday, from
16:30-17:00 every weekday, from 18:00-19:00 every weekday, from
19:00-22:00 on Sundays, Mondays, and Thursdays, and from
22:00-23:00 every weekday. In some embodiments, content
recommendation period times may be made for particular days, such
that content recommendation period times may be made for any day of
the week, month, or year. In some embodiments, content
recommendation period times may be of varying length.
[0050] According to some aspects, the local office 103 may
dynamically change content recommendation period times as the local
office 103 receives additional historical data 328. For example, if
the user 308 begins to watch a sports talk show some weekdays from
07:15-08:00 instead of watching a news program from 07:30-08:00,
then the local office 103 may use this additional historical data
(e.g., the user 308 watching a sports talk show some weekdays from
07:15-08:00) in determining whether to implement a content
recommendation period time from 07:15-08:00 instead of 07:30-08:00
for those weekdays. Additionally, content recommendation periods
may be seasonal. For example, the user 308 may watch professional
football from 19:00-22:00 on Sundays, Monday, and Thursdays during
the professional football season (e.g., from August-February), but
may tend to only access movie content from 19:00-20:00 and
21:00-22:00 on Sundays, Monday, and Thursdays during the months of
March-July. Thus, the local office 103 may assign content
recommendation period times to reflect the change of content
seasons (such as sports seasons, sitcom seasons, and the like).
[0051] At step 504, the local office 103 may determine content
types/classifications, such as for each determined content
recommendation period time of the day. For example, the local
office 103 may determine that the user 308 (or the client device
316) tends to access on the client device 316 a traffic application
at 07:00 every weekday, access a news program from 07:30-08:00
every weekday, access a kids program from 16:30-17:00 every
weekday, access the evening local news from 18:00-19:00 every
weekday, access a sporting event from 19:00-22:00 on Sundays,
Mondays, and Thursdays, access a drama from 22:00-23:00 every
weekday, and access a movie from 20:00-22:00 every Saturday. Thus,
the local office 103 may assign content recommendation periods
(e.g., content recommendation period times and content types) of a
traffic application at 07:00 every weekday, a news program from
07:30-08:00 every weekday, a kids program from 16:30-17:00 every
weekday, the evening local news from 18:00-19:00 every weekday, a
sporting event from 19:00-22:00 on Sundays, Mondays, and Thursdays,
a drama from 22:00-23:00 every weekday, and a movie from
20:00-22:00 every Saturday. In some embodiments, a content type may
also include an application, such as traffic, music, fitness,
gaming, shopping, or weather applications.
[0052] In some embodiments, the local office 103 may assign a
particular content item (e.g., a particular show) to a content
recommendation period time period. For example, if the user 308
tends to primarily access the Today Show from 07:30-08:00 every
weekday (e.g., over 90% of the time), then the local office 103 may
assign the Today Show to that content recommendation period time
period instead of a general news content type.
[0053] At step 506, the local office 103 may determine, using the
historical data 328, user behavior with regard to metadata and
contextual features of content found in the user's history (e.g.,
the historical data 328). As stated above, the historical data 328
may include the behavior of the user 308 regarding how the user 308
accesses and/or interacts with content. Thus, in some embodiments,
the user 308 may vary the length of time the user 308 accesses a
particular content item. As in the above example, the user 308 may
tend to watch a recording of the Today Show 17:00-18:00 every
weekday. However, the local office 103 may determine that the user
308 stops accessing or changes to another content item during some
of the recordings. In other examples, the local office 103 may
determine that the user 308 fast forwards, rewinds, pauses,
records, changes volume, and the like while accessing a content
item. Thus, the local office 103 may determine various behaviors
the user 308 may make while accessing a content item.
[0054] The local office 103 may also determine metadata and
contextual information of the content items found in the historical
data 328. These content items may be items previously accessed by
the user 308, and may be content items in which the user stopped
accessing before the scheduled end of the content item. For
example, the user 308 may tend to watch a recording of the Today
Show 17:00-18:00every weekday. Thus, the local office 103 may
assign a content recommendation period time of 17:00-18:00 every
weekday with a news (or Today Show) content type/classification.
The local office 103 may then determine that the user 308 stops
accessing some of the recordings (e.g., stopping the recording)
before the end of the content recommendation period time. Thus, the
local office 103 may then access contextual information of the
recordings in which the user 308 stopped accessing (or all of the
recordings/content items).
[0055] In one example, the user 308 may stop accessing a recording
(out of several recordings of the Today Show) when there is a
cooking segment using beef. In another example, the user 308 may
fast forward when there is a musical act. In another example, the
user 308 may pause and rewind when there is a weather segment. In
another example, the user 308 may stop accessing a live, recorded,
or on-demand sporting event when the user 308's favorite football
team is losing by a lot of points, or when there are less than 20
highlight play sin a football game, or when teams located far from
the user's location are playing.
[0056] In some embodiments, the user 308 may access a content item
for an entire content recommendation period time period or for even
longer than the content recommendation period time period. For
example, professional football games are generally 3 hours in
length. However, in some cases, a game may last longer than 3 hours
(such as due to it going into overtime, a lot of injury timeouts, a
lot of incomplete passes, an event scheduled before the game going
over its scheduled time slot, etc.). Accordingly, in some cases,
the user 308 may continue to watch a football game past a content
recommendation period time period (such as past 22:00) and into
another content recommendation period time period. Thus, contextual
features from that game may be analyzed to determine whether any of
those contextual features relate to the user's behavior (i.e.,
continuing to watch the game and/or not going to a recommended next
content recommendation period). For example, the user may continue
to watch the game if the game is very close, if the user's favorite
team is playing, or if it is a special game (such as a championship
game).
[0057] In some embodiments, the user 308 may tend to always access
(e.g., watch live, watch on-demand, or record) a content item if
the content item is a new episode of a show. Thus, the local office
103 may analyze metadata of various content items and determine
whether there are any "new" episodes. Similarly, the user 308 may
tend to never access a content item if the content item is not a
new episode of a show. For example, the user 308 may always switch
to and watch a linear transmission of a new episode of "Suits"
during a time slot of 21:00-22:00, but may not switch to and watch
a linear transmission of a repeat episode of "Suits" during that
time slot.
[0058] At step 508, the local office 103 may perform sensitivity
analyses and machine learning techniques to determine the metadata
and contextual features relating and/or corresponding to a user's
behavior and/or expected/predicted user behavior. Thus, there may
be many contextual features in a content item. According to some
aspects, the local office 103 may determine a correlation between
contextual features of applications (such as weather, gaming,
shopping, or traffic, etc.) and a user's behavior. Such contextual
features may include whether there is a cooking segment, whether
there is a chase scene in a program, whether the score of a
football game is 35-7, whether it is raining outside (e.g., derived
from a weather application), whether there was a world record set
in an Olympic event such as speed skating, etc.
[0059] Accordingly, for a given content item, such as a football
game, the local office 103 may determine which contextual features
may mean more to a user's behavior (e.g., more likely to affect a
user's behavior, such as switching channels). These contextual
features may include which teams are playing, the score, the time
remaining, and the like. For example, the local office 103 may
determine that if the user 308's favorite team is playing, the user
will tend to abandon the game less frequently than if the user
308's favorite team was not playing. Also, the local office 103 may
determine that when the user 308's favorite team is winning, the
user 308 abandons the game even less frequently when the user 308's
favorite team is just playing (i.e., when the team could be either
winning, losing, or tied).
[0060] In one example, the user 308 may tend to stop accessing the
Today Show when there is a cooking segment while accessing the show
on any platform (e.g., live, recorded, on-demand, etc.). Thus, the
local office 103 may determine that the user 308 abandons some of
the recordings of the Today Show. The local office 103 may then
analyze and determine contextual features of those recordings in
which the user 308 stop accessing. These contextual features may
include features such as the hosts of the Today Show hosting
outside the studio during some of the recordings, a musician
performing during some of the recordings, holiday decorations,
whether or not there is a weather segment, and the like. Some or
all of these contextual features may or may not relate to and/or
cause the user 308's behavior. To determine which contextual
features relate to the user 308's behavior, the local office 103
may determine which contextual features tend to frequently show up
across the recordings of the Today Show. As in the above example,
the local office 103 may determine that there may be similarities
between the episodes in which the user 308 stops accessing, such as
all of the episodes contain a cooking segment. The local office 103
may determine when a contextual feature appears/occurs in a
recording, and then may determine those contextual features having
the highest number of appearances/occurrences (e.g., ranking the
contextual features based on occurrence). Higher ranked contextual
features may then be determined to be the contextual features that
affect the user 308's behavior (e.g., abandoning the recording of
the Today Show when there is a cooking segment).
[0061] In one example, out of 100 recordings of the Today Show, the
user 308 may have abandoned the show 40 times, and out of those 40
times, there has been a cooking segment 35 times, there has not
been a weather segment 20 times, there has been a musical
performance 4 times, the hosts have hosted outside 8 times, and the
like. From these four contextual features, the local office 103 may
determine that the user tends to abandon when there is a cooking
segment. In some embodiments, a user's behavior may be based on a
combination of features. For example, if out of the 35 recordings
in which a cooking segment aired, 20 of them did not have a weather
segment. Therefore, the local office 103 may determine that the
user is even more likely to abandon when there a recording of the
Today Show that contains a cooking segment and does not a weather
segment than when there is only a cooking segment.
[0062] In some embodiments, the local office 103 may also determine
at which point during the playback of the Today Show the user 308
stopped or abandoned the playback (e.g., stopped accessing). The
local office 103 may use this information in determining which
contextual features affect the user 308's behavior. The local
office 103 may determine what contextual features happened
immediately before and immediately after the point in which the
user 308 stopped accessing the recording to determine any
pattern(s) in the user's behavior. Additionally, the point/time in
the playback in which the user 308 abandoned (or other behavior
such as fast forward, rewind, pause, record, modify volume, voice
command, etc.) the content item may help in narrowing down a
collection of many different contextual features. Also, the local
office 103 may give a higher weight or preference to contextual
features that are closer to an abandoning point than contextual
features that are farther away from the abandoning point.
[0063] In some embodiments, the local office 103 may analyze
contextual features of applications running on the client device
316. For example, the local office 103 may determine from a weather
application that the weather is good. In other situations, the
local office 103 may obtain weather from other sources, such as
from broadcasted content (e.g., from a weather channel), by having
a user input the weather, and the like. In some situations, the
local office 103 may obtain the weather via a weather station
connected to a premises that contains (or otherwise associated
with) the client device 316. Such a weather station may be part of
a home automation system that may be maintained and/or accessible
by the local office 103. Good weather may include situations in
which a percentage of rain for the day may be less than 30%, the
temperature may be around the average mark for that time of year or
season of the year, and the like. In other embodiments, good
weather may include situations in which a deviation from an
expected weather condition satisfies a threshold. For example, in
Chicago during winter, one may expect the weather to be cold and
snowy. Thus, a good weather condition may be cold and snowy.
However, in the summer, cold and snowy may be a bad weather
condition. Again, using statistical techniques, the local office
103 may determine which types of weather may affect a user's
behavior. For example, if the weather is good, then the user 308
may watch television longer than the user would if the weather was
not good for that day. For example, it may take the user 308 a
shorter time to get to work if the weather is good, thus giving him
more time to consume/access content (e.g., watch television).
Similarly, the local office 103 may determine other external
environmental factors in a similar manner as weather. For example,
the local office 103 may determine (e.g., via a traffic application
or other method) that if the traffic for the user 308's commute is
good for that day (e.g., traffic moving at substantially the speed
limit and/or no accidents, and the like), then the user 308 may
watch television longer than the user would if the traffic was not
good for that day.
[0064] In another example, the local office 103 may determine using
statistical techniques, the local office 103 may determine which
types of video game outcome may influence a user's behavior from
that the user 308 is doing poorly in a video game. For example, if
the user 308 is accessing a gaming application running on the
client device 316, and the user 308 is doing poorly in a video
game, the user 308 may abandon the gaming application and switch to
an on-demand horror movie. Similarly, if the user 308 is doing
great in a video game, the user 308 may continue playing the video
game on the gaming application, and may not switch to other content
on the client device 316. In another example, the local office 103
may determine from a fantasy football sporting application that the
user 308's fantasy football team is doing very well (e.g., scoring
more points than any other team in the fantasy football league),
and thus the user 308 may access a program corresponding to a mood
of the user 308, such as a comedy show. If the user 308's fantasy
football team is doing poorly, then the user 308 may access sadder
content, such as a dark drama or horror program.
[0065] At step 510, the local office 103 may determine the user
profile and/or content recommendation period schedule based on the
results of both step 506 (e.g., the user behavior with regard to
metadata and contextual features of content found in the user's
history, as well as metadata and contextual information of the
content items found in the historical data 328), and step 508
(e.g., the contextual features relating to a user's behavior and
expected/predicted user behavior). Thus, each content
recommendation period's content type/classification and/or time
period may be specific to contextual features of content accessed
by the user 308, and/or specific to the behavior of the user 308
related those contextual features. Thus, for each content
recommendation period, there may be several candidates for content
type and several candidates for time period, which may be based on
the contextual features of a content item, like a show. This will
be further described below in more detail. The process 500 may then
end at step 512. According to some aspects, the process 500 may end
after any of the steps in the process 500. Any of the disclosed
steps in FIG. 5 may be omitted, be performed in other than the
recited order, repeated, and/or combined.
[0066] Returning to FIG. 4, and after determining a profile for the
user 308 (or for the client device 316), the process 400 may
continue to step 408. At step 408, the local office 103 may
determine/recognize a user interaction with the client device 316.
A user interaction may be a situation in which a user, such as the
user 308, may interact with the client device 316 and/or may desire
to access (e.g., watch) content using the client device 316. For
example, the user 308 may turn on a television and set-top box, and
may watch a television program. In another example, the user 308
may access an application, such as weather, traffic, music, gaming,
or internet applications.
[0067] In some embodiments, the user 308 may interact with the
client device 316 using the input device 314. For example, the user
308 may use a remote control to enter a command (e.g., make a
content selection) into the client device 316. The user 308 may do
this by entering a number key, an operations key (e.g., "Enter,"
"Select," etc.), or any other key on the remote. Thus, the local
office 103 may determine that the user has interacted with the
client device 316 after the local office 103 receives a message
from the client device 316 that includes which command the user 308
entered. In some embodiments, the local office 103 may then
transmit content to the client device 316 in response to this user
interaction (e.g., in response to the remote control command).
[0068] In some embodiments, the user 308 may interact with the
client device 316 via a motion sensor, which may be the input
device 314, and may be operably connected to the client device 316.
In this situation, the motion sensor may detect a user's presence,
a user's movement, when a user approaches the client device 316 or
to the motion sensor, and the like. In some embodiments, there may
be a threshold associated with the motion, such that the client
device 316 or the input device 314 may detect a user interaction
after the user 308 moves at a particular speed. In some situation,
there may also be threshold proximity, such that the client device
316 or the input device 314 may detect a user interaction after the
user 308 moves within a particular proximity of the client device
316 or the input device 314. The client device 316 may then
transmit a message to the local office 103 indicating this user
interaction.
[0069] According to some aspects, the client device 316 may detect
user interaction after a user may have moved the input device 314,
such as by picking up a remote. According to some aspects, the
client device 316 or the input device 314 may detect vibration as a
user interaction, such as when a person walks, closes a door, or
opens a door. In these situations, the input device 314 may
comprise an accelerometer or other element configured to detect
movement of the input device 314. The client device 316 may then
transmit a message to the local office 103 indicating this user
interaction.
[0070] According to some aspects, the client device 316 may detect
user interaction after detecting a computing device and/or wireless
device, such as a smartphone, laptop, tablet, smartwatch, or
Bluetooth device (e.g., devices 302, 304, or 306). For example, the
user 308 may be associated (e.g., own or possess) with a smartphone
(the device 302), the user 310 may be associated with a Bluetooth
device (the device 304), and the user 312 may be associated with a
tablet (the device 306). The users 308, 310, and 312 may have
previously registered their respective devices with the local
office 103 (such as via a cable or internet account). Thus, after
the client device 316 detects the device 302, the client device 316
may register a user interaction for the user 308. Similarly, after
the client device 316 detects the device 304 and the device 306,
the client device 316 may register a user interaction for the user
310 and the user 312. Detecting various users may trigger the
client device 316 and/or the local office 103 to implement
corresponding profiles (e.g., profiles 322, 324, and 326). In such
situations, the client device 316 and/or the local office 103 may
determine which profile to implement, or may implement more than
one profile. This will be described below in more detail. In one
example, the user 308 may drive the user 308's car into the user
308's garage, and the car may join (e.g., via a wireless
connection) the network 318 and/or connect to the user 308's home
automation system. The client device 316 may then detect the user
308's car and/or that the user 308's car has connected to the
network 318 or the home automation system, and thus may register a
user interaction for the user 308 based on the detection of the
user 308's car.
[0071] According to some aspects, the user 308 and/or the device
304 may leave a proximity of the client device 316 (or some other
means of not being detected by the local office 103 and/or the
client device 316), and thus the client device 316 may no longer
detect the user 308 and/or the device 304. In such situations, the
client device 316 and/or the local office 103 may then optionally
stop implementing a profile (e.g., the user profile 322) associated
with the user 308, and may implement a profile (e.g., the user
profile 324) associated with any other detected user (e.g., the
user 310). Similarly, if the user 308 then returns to be within a
close proximity (or otherwise be detected) to the client device
316, the local office 103 may optionally begin to implement the
profile associated with the user 308 (e.g., the user profile 322).
Such implementation may be based on a hierarchy associated with the
profiles (which will be discussed later). Thus, the local office
103 may dynamically change the implementation of profiles.
[0072] According to some aspects, the client device 316 may detect
user interaction via a prompt. For example, local office may
display a on a display of the client device 316 asking whether a
user is interacting with the client device 316 or whether a user
would like to load a particular profile. In such situations, a user
may select, using an input device 314 such as a remote, keyboard,
mouse, touch screen, a profile to load onto the client device 316.
The profile may be specific to a user or client device, depending
on the user's selection.
[0073] Other methods of interacting with the client device 316
include using a microphone or camera. With the microphone, the
client device 316 and/or the local office 103 may perform speech
recognition to detect a command and/or a particular user. For
example, the user 308 may want to access a weather application or a
weather related program, and may say "weather" into a microphone.
The local office 103 may then provide content or a recommendation
based on this command. In the case of a camera, the client device
316 may perform facial recognition to determine a particular
user.
[0074] In some embodiments, the local office 103 may assign a
profile form among a plurality of different profiles based on which
type of user interaction the local office 103 and/or the client
device 316 detected. For example, if the time is 07:00, and the
user 308 says "weather" into a microphone (e.g., the input device
314), that user interaction may trigger a particular profile for
the user 308. This profile may be, for example, a work day profile,
because the local office 103 may determine that the user 308 tends
to say "weather" into the microphone before going to work most
days. The local office 103 may determine that the user 308 is going
to work based on a GPS device or the user's viewing patterns (e.g.,
not accessing content on the client device 316 during work hours).
In another example, a microphone (e.g., the input device 314) may
detect other external environmental factors, such as a barking dog
at the time of 15:00, because a mailman is approaching the user
308's house. Because mail may be delivered on particular days of
the week (e.g., Monday-Friday or Monday-Saturday), a profile for
those particular days may be implemented based on the detecting of
the barking dog at the time of 15:00. The local office 103 may use
other external environmental factors according to aspects disclosed
herein.
[0075] Additionally, a profile may be selected based on contextual
features of applications. For example, the local office 103 may
determine that the user 308 tends to work from home on snowy days.
The local office 103 may then assign a snowy day profile based on
based on a weather application running on client 316 indicating it
is snowing or going to snow. Thus, the snowy day profile may
include content recommendation periods during the day at times in
which the user 308 may usually be at work (such as on a non-snowy
day). Additionally, a snowy day profile may contain content
recommendation periods having content types/classifications
corresponding to the contextual features obtained by the local
office 103. For example, content types for a snowy day profile may
include content associated snow, such as programs related to
Christmas, winter, snowmen, hot chocolate, salt and ice melting,
storm preparation, and the like. In another example, the local
office 103 may determine that the user 308 tends to access content
with little contextual variance (e.g., SpongeBob) while accessing a
shopping application. The local office 103 may then assign a
shopping profile (e.g., a profile with content having little
contextual variance) when the user 308 accesses a shopping
application.
[0076] At step 410, the local office 103 may, in response to
detecting a user interaction, assign a content recommendation
period based upon a selected profile (e.g., profiles 322, 324, or
326). Depending on which profile the local office 103 implements,
the local office 103 may implement a content recommendation period
from that selected profile. As stated above, a profile may be
composed of different content recommendation periods. Each content
recommendation period may have a content recommendation period time
(e.g., start time or time period) and/or an assigned content
type/classification (e.g., news, kids, sports, weather application,
etc.).
[0077] Thus, after the local office 103 detects a user interaction
and assigns a profile, the local office 103 then may assign a
content recommendation period having a content recommendation
period time period that may include the current time. For example,
if the current time is 16:06, and the selected profile (e.g., the
user profile 322) has a content recommendation period with a
content type of kids and a content recommendation period time
period of 16:00-16:30, then the local office 103 may assign this
content recommendation period. Additionally, if the current time is
16:00, then the local office 103 may also assign this content
recommendation period with a content recommendation period time
period of 16:00-16:30.
[0078] In other cases, the local office 103 may assign a content
recommendation period having a content recommendation period start
time that is about to begin and/or approaching (e.g., in the
future). For example, the user 308 may tend to not access content
from the client device 316 between 23:00-07:00, but may have a
content recommendation period for a weather application starting at
07:00. In this situation, if the client device 316 detects a user
interaction at 06:55, then the local office 103 may assign the
content recommendation period for the weather application because
the content recommendation period time of 07:00 is approaching.
[0079] In some embodiments, the local office 103 may implement a
plurality of profiles at step 408, such as profiles 322, 324, and
326 corresponding to the users 308, 310, and 312. Thus, at step
410, the local office 103 may optionally assign a content
recommendation period from one of these profiles. In some cases, a
user may select (e.g., via a prompt provided by the client device
316) one of the profiles 322, 324, and 326 to implement. In other
situations, the local office 103 may determine a consensus content
recommendation period that may satisfy all of the assigned
profiles. The local office 103 may determine a consensus content
recommendation period using a predetermined hierarchy, which may
have been established by one or more users, or may be determined by
the local office 103. The local office 103 may determine the
hierarchy based on a how frequent a profile is implemented on the
client device 316. For example, after selecting profiles 322, 324,
and 326, the local office 103 may select the user profile 322 if
the user profile 322 has been implemented more frequently than
either of profiles 324 and 326. Additionally, after implementing
the user profile 322, the local office 103 may still recommend
content items (such as later in the process 400) using information
from the user profile 322's content recommendation periods (e.g.,
timing and/or content types), but also accounting for historical
data of other users, such as the historical data 330 and 332
corresponding to users 310 and 312. This will be described below in
more detail.
[0080] In some embodiments, the local office 103 may assign a
particular content recommendation period based on the type of user
interaction. For example, if the user 308 approaches client 316 or
if client 316 detects a wireless device of the user 308 (e.g., the
device 302), then the user 308 may not necessarily desire to watch
a video, and client 316 may assign an application content
recommendation period (such as by displaying traffic, weather,
social media, time, etc.). If, for example, the user uses a remote
to access content, then the local office 103 may assign a
programing content recommendation period (such as news, movie,
kids, etc.) instead of an application content recommendation
period.
[0081] At step 412, the local office 103 may determine the amount
of time remaining in an assigned content recommendation period. For
example, if the current time is 16:06, and the selected profile
(e.g., the user profile 322) has a content recommendation period
with a content type of kids and a content recommendation period
time period of 16:00-16:30, then the local office 103 may assign
this content recommendation period and determine that there are 24
minutes left in this content recommendation period. Similarly, the
local office 103 may also determine the amount of time until the
next content recommendation period. For example, the user 308 may
tend to not access content from the client device 316 between
23:00-07:00, but may have a content recommendation period for a
weather application starting at 07:00. In this situation, if the
client device 316 detects a user interaction at 06:55, then the
local office 103 may determine that there is 5 minutes until the
weather application content recommendation period. In such
situations, the local office 103 may then assign the weather
application content recommendation period at 06:55 or may wait
until 07:00 to assign it. In some cases, local office may assign
one content item for the 5 minutes, and then assign the weather
application at 07:00.
[0082] At step 414, after selecting a content recommendation period
having a content recommendation period time and content type, and
after determining the amount of time remaining in the selected
content recommendation period, the local office 103 may analyze and
retrieve prospect content assets whose content type may correspond
to the assigned content recommendation period's content type and/or
whose length may correspond to the time remaining in the content
recommendation period. For example, if the current time is 16:06,
and the selected profile (e.g., the user profile 322) has a content
recommendation period with a content type of "kids" and a content
recommendation period time period of 16:00-16:30, then the local
office 103 may assign this content recommendation period and
determine that there are 24 minutes left in this content
recommendation period. The local office 103 may then retrieve
content assets, such as from content assets analyzed in step 402 or
other content received from the content sources 334, having a
content type of "kids" and a length of approximately 24 minutes. In
some cases, a selected content item's length may not exactly match
a time remaining in a content recommendation period. In these
situations, the local office 103 may retrieve content having length
that is in the vicinity of the time remaining in the content
recommendation period (e.g., substantially matches). In the above
example, the local office 103 may retrieve content items having a
length of 20-40 minutes. However, the local office 103 may give
precedence to content items having a length that most closely
matches the time available in a content recommendation period over
other content items.
[0083] The analyzed content assets may include assets configured
for any content platform, such as internet, linear, DVR, on-demand,
music, and applications (e.g., traffic, weather, social media). In
some embodiments, the content assets may be on-demand or DVR
platform assets, and a user may have the ability to access these
on-demand or DVR assets at any time. Thus, the local office 103 may
be able to recommend on-demand or DVR assets for access by a user
at any time during a profile's schedule. In some embodiments, the
content assets maybe linear or live platform assets, and a use may
only have the ability to access these assets during a live
broadcast. Thus, the local office 103 may be able to recommend
linear assets for access by a user during a live broadcast of those
linear assets. This will be discussed below in more detail with
regard to the recommendations made by the local office 103.
[0084] At step 416, the local office 103 may then determine, assign
weights to, and rank candidate content assets from the prospect
content assets. The local office 103 may determine these candidate
content assets by assigning weights to prospect content assets
based on analyzing metadata and contextual features of the prospect
content assets and determining the amount of correlation between
the metadata and contextual features and a user's profile (e.g.,
the user profile 322), historical data (e.g., the historical data
328), and/or behavior. The local office 103 may have previously
analyzed the metadata and contextual features of the prospect
content assets in step 402, but may also analyze the metadata and
contextual features of the prospect content assets here at step
416. The local office 103 may then rank the candidate content
assets according to their assigned weight.
[0085] In one example of the local office 103 assigning weights
based on metadata and contextual features, the user profile 322
and/or the historical data 328 may indicate that the user 308 tends
to watch professional football on Mondays from 19:00-22:00,
Thursdays at 20:00-23:00, and Sundays at 19:00-22:00. The user
profile 322 may also indicate that the user tends to watch football
games that include the user's favorite team, and tends keep
watching games that include the user's favorite team until the end
of the content recommendation period time. In this case, football
games that include the user's team may be weighted higher than
other football games.
[0086] Continuing with the previous example, the user profile 322
may also indicate that the user 308 tends to switch from the user's
favorite team's football game if the user's favorite team is losing
by 21 or more points, but may tend to watch any football game if
both the score of the two teams is within 7 points and there has
been at least 20 highlight plays in the game. Thus, if the local
office 103 retrieves prospect content assets containing 1) a live
football game having the user 308's favorite team losing by 28
points, and 2) a live football game not including the user 308's
favorite team, but having a score of the two teams within 7 points
and there has been 24 highlight plays in the game, then the local
office 103 may give a higher weight to the second game with the
score of the two teams within 8 points and having at 24 highlight
plays in the game.
[0087] In another example, the local office 103 may determine that
profile 322 and/or the historical data 328 may indicate that the
user 308 tends to watch professional football on Mondays from
19:00-22:00, Thursdays at 20:00-23:00, and Sundays at 19:00-22:00,
and access a drama from 22:00-23:00 every day of the week. Profile
322 and/or the historical data 328 may indicate that whenever there
is a new episode of Downton Abbey on Sundays at 22:00, the user 308
almost always watches the whole episode of Downton Abbey for the
entire length of the content recommendation period time of
22:00-23:00. However, professional football games sometimes go into
overtime, and may last longer than an expected time (e.g., more
than 3 hours). Thus, if the game has previously aired and the user
is accessing it on DVR or on-demand, the local office 103 may
analyze the metadata and contextual features of the football game
and determine that the game lasted longer than three hours. The
local office 103 may then provide the football game having a
shorter length that fits into the content recommendation period's
time period and/or ending at the end of the content recommendation
period's time period, and thus not interfering with the next
content recommendation period of Downton Abbey at 22:00.
[0088] In some situations, the user 308 may interact with the
client device 316 in the middle of the content recommendation
period time of 19:00-22:00 for professional football on Sunday. In
these situations, the local office 103 may retrieve a content asset
(e.g., football game) matching the remaining time left in the
content recommendation period. Alternatively, the local office 103
may retrieve a shortened version of the game (e.g., such as only
showing scoring plays or highlights, only showing the user's
favorite team's offensive possessions, and the like, which may be
derived from the user 308's behavior in relation to contextual
features of the content asset) to substantially match the available
time left in the content recommendation period.
[0089] In another example, local device 103 may determine the user
profile 322 indicating that the user 308 tends to watch a recording
of the Today Show from 17:00-18:00 every weekday. The user profile
322 may also indicate that if there is a cooking segment during the
recorded broadcast of the Today Show, the user tends to switch from
the recording of the Today Show to a 24 hour news channel (e.g.,
MSNBC) until the end of that content recommendation period (18:00).
At 18:00, the user 308 may then tend to watch the local evening
news until 19:00. Thus, after the client device 316 records the
Today Show, the local office 103 may analyze and determine the
metadata and contextual features of that recording. These
contextual features may include whether the hosts of the Today Show
hosted outside the studio, whether a musician performed, whether
there was a holiday theme, whether there was a mistake made by a
host, whether or not there was a weather segment, and the like. In
this case, the local office 103 may determine that there was a
cooking segment during the episode. Thus, referring to the user
profile 322 which indicates that the user 308 tends to switch to
MSNBC when there is a cooking segment during the recording of the
Today Show, the local office 103 may provide a higher weight to
MSNBC than to the recording of the Today Show for that content
recommendation period having the content recommendation period time
of 17:00-18:00.
[0090] In another example, the local office 103 may weight prospect
content items based on contextual features derived from
applications running on the client device 316. For example, the
local office 103 may determine from a weather application that the
current weather is snowy. In this case, the local office 103 may
provide a higher weight to content items associated with snow, such
as movies or shows set in the winter or around Christmas.
[0091] In some cases, weights may be assigned to content based on
the popularity of a program, such as by using ratings, relevancy
rankings, or any other method of determining popularity. The
popularity of content may also be based on users within a
particular proximity to the user 308, across demographics (e.g.,
age, sex, income, location, etc.), and the like.
[0092] At step 418, the local office 103 may then transmit to the
client device 316 an indication of one or more of the higher ranked
candidate content assets. The local office 103 may make such an
indication with a recommendation for a particular candidate content
asset. For example, the client device 316 may provide a prompt on a
display recommending to the user 308 one or higher ranked candidate
content assets for an assigned content recommendation period. In
other situations, the local office 103 may transmit to the client
device 316 the one or more of the higher ranked candidate content
assets. In some cases, the local office 103 may indicate only a
highest ranked candidate content asset. Additionally, the client
device 316 may automatically access (e.g., play) one of the
candidate content assets (such as a highest ranked candidate).
[0093] According to some embodiments, an indication and/or
recommendation can be based on any data that may be available to
local officer 103, such as any information that may have been
gathered and/or any event (or interaction) that has or is currently
happening. For example, an indication and/or recommendation may be
based on a portion of a profile, a portion of the content
recommendation period, and the like.
[0094] In one example, local device 103 may determine the user
profile 322 indicating that the user 308 tends to watch a recording
of the Today Show 17:00-18:00 every weekday. The user profile 322
may also indicate that if there is a cooking segment during the
recorded broadcast of the Today Show, the user tends to switch from
the recording of the Today Show to watch a 24 hour news channel
(e.g., MSNBC) until the end of that content recommendation period
(18:00). In this case, the local office 103 may determine that
there was a cooking segment during the episode. According,
referring to the user profile 322 which indicates that the user
tends to switch to MSNBC when there is a cooking segment during the
recording of the Today Show, the local office 103 may provide a
higher weight to MSNBC than to the recording of the Today Show.
Thus, the local office 103 may recommend watching MSNBC during the
content recommendation period from 17:00-18:00 instead of the Today
Show. In this example, the content type of the Today Show (e.g.,
news) may match the content type of MSNBC (e.g., news). However, in
another example, the user 308 may tend to switch to SpongeBob
(e.g., kids content type) when the recording of the Today Show
features a musical act. Thus, if the recording of the Today Show
features a musical act, the local office 103 may recommend watching
SpongeBob during the content recommendation period from 17:00-18:00
instead of the Today Show or MSNBC, because SpongeBob would be have
a higher ranking or weight based on the local office 103's analysis
of the Today Show's contextual features.
[0095] In another example, the local office 103 may analyze
contextual features of applications running on the client device
316. The local office 103 may determine using a weather application
that the weather is good (e.g., not raining). The user profile 322
may indicate a content recommendation period for 07:30-08:00 for
news programming. The user profile 322 may also indicate that if
the weather is good, then the user 308 may watch television longer
than the user would if the weather was not good for that day.
Accordingly, after the local office 103 analyzes the weather and
determines that the weather is good, then the local office 103 may
recommend additional content for the user 308 to access, because,
as indicated in the user profile 322, the user 308 tends to watch
more television when there is good weather. The additional content
may also be based on the historical data 328, which may indicate
specific types of content the user tends to watch. In some cases,
the local office 103 may assign the weather a value, such as from 1
to 10, with 1 being the worst weather and 10 being the best
weather. In these circumstances, the amount of extra time available
for the user 308 to watch television may correspond to the assigned
weather value. For example, if the weather is assigned a 10, then
the user may have an extra 30 minutes to watch television. If the
weather is assigned a 7, the user may have an extra 5 minutes to
watch television. If the weather is assigned a 5, the user may not
have any extra time to watch television, and may follow the content
recommendation period time of 07:30-08:00. If the weather is
assigned a 1, then the user may have to leave 15 minutes earlier,
and may have 15 minutes less to watch television. In the situation
where the weather is assigned a 10, the local office 103 may
recommend a content asset to fill a one hour time period (e.g.,
from 07:30-08:30). Alternatively, the local office 103 may
recommend a content asset for the original content recommendation
period time of 07:30-08:00, and then pick one or more additional
content assets to fill the remainder of the time. A similar
approach may be used for when weather is assigned a 7 with 5 extra
minutes. If the weather is assigned a 1, then the local office 103
may pick one or more content assets to fill the 15 minutes (e.g.,
from 07:30-07:45), which may be of the same content type as
indicated in the content recommendation period.
[0096] In another example, the local office 103 may determine using
a traffic application that the traffic is good (e.g., traffic
moving at substantially the speed limit and/or no accidents). The
user profile 322 may indicate a content recommendation period for
07:30-08:00 for news programming. The user profile 322 may also
indicate that if the traffic is good, then the user 308 may watch
television longer than the user would if the traffic was not good
for that day. Accordingly, after the local office 103 analyzes the
traffic and determines that the traffic is good, then the local
office 103 may recommend additional content for the user 308 to
access, because the user 308 tends to watch more television when
the traffic is good. Additionally, as in the above weather example,
the local office 103 may assign the traffic a value, such as from 1
to 10, with 1 being the worst weather and 10 being the best
weather. The local office 103 may then base the amount of time
available for extra content on this assigned value, and then
determine corresponding content to recommend.
[0097] In another example, the user profile 322 may indicate that
the user 308 tends to always watch a specific contextual feature,
such as a hockey fight between four or more hockey players. Thus,
the local office 103 may transmit a recommendation for (or
automatically tune to) a content asset containing a hockey fight
between four or more hockey players. In these situations, the
recommended content may not match time period and/or content type
of a currently assigned content recommendation period. However, the
local office 103 may make this recommendation because of the very
close correlation between the user 308's behavior (e.g., always
watching a fight between four or more hockey players) and the
contextual features of a content item (e.g., content having a fight
between four or more hockey players).
[0098] According to some aspects, the local office 103 may examine
a social media application on the client device 316 for contextual
features that may relate to the user 308's behavior. For example,
the local office 103 may analyze social media feeds or posts from
friends of the user 308 or people unknown to the user 308 to
determine contextual features in these feeds or posts. Such
contextual features may include internet content, popular
television shows or scenes from those shows, popular movies or
scenes from those movies, popular sporting events or plays/acts
from those events, and the like. From these contextual features,
the local office 103 may then recommend content according to the
user 308's behavior. For example, the user profile 322 may indicate
that the user 308 tends to always watch a specific contextual
feature, such as a hockey fight between four or more hockey
players. If the local office 103 determines from the contextual
features of a social media application that a hockey fight between
four or more hockey players is currently airing, then the local
office 103 may transmit a recommendation for (or automatically tune
to) the currently airing content asset containing a hockey fight
between four or more hockey players. Alternatively, if the hockey
fight is on a video-sharing website or on-demand platform, then the
local office 103 may recommend content on those mediums and/or
platforms to the user 308.
[0099] According to some aspects, the client device 316 may detect
user interaction after detecting more than one user (e.g., the
users 308 and 310), and thus may determine consensus content
candidates. Detecting various users may trigger the client device
316 and/or the local office 103 to implement corresponding user
profiles (e.g., profiles 322 and 324). In such a situation, a user
may select (e.g., via a prompt provided by the client device 316)
one of the profiles 322 or 324 to implement. In other situations,
the local office 103 may determine a profile to implement using a
hierarchy (discussed above). The local office 103 may further
analyze the historical data 328 and 330 (corresponding to the users
308 and 310) to determine user behavior and/or viewing patterns of
the users 308 and 310. After selecting a profile to implement
(e.g., the user profile 322), the local office 103 may assign
weights to prospect content assets based on the behavior and
viewing patterns of both the users 308 and 310. Thus, the user
profile 322 may indicate a content recommendation period with a
content recommendation period time of 18:00-19:00 and a content
type of local news, and may assign this content recommendation
period based on the current time. However, the local office 103
also recognizes the user 310 and may weight content items
differently than if the user 310 was not detected. Thus, the local
office 103 may determine that the user 310 tends to primarily watch
a sitcom from 18:00-19:00, and then may next tend to watch a sports
program from 18:00-19:00 (e.g., the user 310 tends to watch a
sitcom 75% of the time from 18:00-19:00, tends to watch a sports
program 20% of the time from 18:00-19:00, and tends to watch other
programs the other 5% of the time). The local office 103 may also
determine that the user 308 tends to primarily watch the local news
from 18:00-19:00, and then may next tend to watch a sports program
from 18:00-19:00 (e.g., the user 308 tends to watch the local news
85% of the time from 18:00-19:00, tends to watch a sports program
10% of the time from 18:00-19:00, and tends to watch other programs
the other 5% of the time). Thus, determining that each user's
second option for watching television between 18:00-19:00 is the
same, the local office 103 may provide greater weight to sports
program content items, and subsequently, recommend a sports program
content item. Additionally, the recommendation may further be
refined if the particular sports program one user tended to watch
matched the particular sports program the other user tended to
watch.
[0100] At step 420, the local office 103 may then track and
determine a user's viewing patterns and/or behavior associated with
the content indication or recommendation made by the local office
103 in step 418. For example, the user 308 may not accept a
recommendation/indication prompt from the local office 103, and may
actually access other content. In these cases, the local office 103
may transmit additional recommendations/indications of content
items which may be weighted less than candidate content item
transmitted in an initial or previous recommendation/indication. In
another example, the user 308 may accept/select a
recommendation/indication prompt for a content asset transmitted by
the local office 103, and local office 103 (or some other content
provider) may then transmit content that may correspond to the
selected content asset (e.g., the selected program, application,
etc.). Another example of user behavior may be that the user 308
did not stop accessing a recommended content item until the end of
the content recommendation period (e.g., did not turn or switch to
other content). Also, the user 308 may continue to watch a content
item that runs over the scheduled content recommendation period.
Additionally, the local office 103 may determine that the user 308
stopped accessing a content item (e.g., a content item transmitted
by local office 103 selected from an indication/recommendation
transmitted by local office 103, a content item that client device
302 automatically tuned to based on an indication/recommendation
transmitted by local office 103, and the like), and at which point
during the playback of a content item the user 308 stopped the
playback. Any behavior or viewing pattern of a user may be
determined in step 420.
[0101] The process 400 may then return to step 404, where the local
office 103 may use the information determined and obtained in step
420 to update, determine, gather, and/or analyze historical data
(e.g., the historical data 328) for a user (e.g., the user 308).
The process 400 may then continue and may use the updated
historical data. The process 400 may end after any of the steps in
the process 400. Any of the disclosed steps in FIG. 4 may be
omitted, be performed in other than the recited order, repeated,
and/or combined.
[0102] Although example embodiments are described above, the
various features and steps may be combined, divided, omitted,
rearranged, revised and/or augmented in any desired manner,
depending on the specific outcome and/or application. Various
alterations, modifications, and improvements will readily occur to
those skilled in art. Such alterations, modifications, and
improvements as are made obvious by this disclosure are intended to
be part of this description though not expressly stated herein, and
are intended to be within the spirit and scope of the disclosure.
Accordingly, the foregoing description is by way of example only,
and not limiting. This patent is limited only as defined in the
following claims and equivalents thereto.
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